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Pytorch Widedeep

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Pytorch Widedeep

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提交 d490ae12 编写于 作者: H Hyo-kyun Park 提交者： hyokyun-park
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make them like origin version

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demo2_building_blocks.ipynb
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因为 它太大了无法显示 source diff 。你可以改为 查看blob。
demo3_using_it.ipynb
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......@@ -636,121 +636,31 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1 of 100, Loss: 246.073\n",
"Epoch 2 of 100, Loss: 94.48\n",
"Epoch 3 of 100, Loss: 126.0\n",
"Epoch 4 of 100, Loss: 115.982\n",
"Epoch 5 of 100, Loss: 133.325\n",
"Epoch 6 of 100, Loss: 158.583\n",
"Epoch 7 of 100, Loss: 54.36\n",
"Epoch 8 of 100, Loss: 79.254\n",
"Epoch 9 of 100, Loss: 182.114\n",
"Epoch 10 of 100, Loss: 51.386\n",
"Epoch 11 of 100, Loss: 104.073\n",
"Epoch 12 of 100, Loss: 143.09\n",
"Epoch 13 of 100, Loss: 70.531\n",
"Epoch 14 of 100, Loss: 97.966\n",
"Epoch 15 of 100, Loss: 59.099\n",
"Epoch 16 of 100, Loss: 94.067\n",
"Epoch 17 of 100, Loss: 183.913\n",
"Epoch 18 of 100, Loss: 47.979\n",
"Epoch 19 of 100, Loss: 179.66\n",
"Epoch 20 of 100, Loss: 137.305\n",
"Epoch 21 of 100, Loss: 127.122\n",
"Epoch 22 of 100, Loss: 229.17\n",
"Epoch 23 of 100, Loss: 151.097\n",
"Epoch 24 of 100, Loss: 161.884\n",
"Epoch 25 of 100, Loss: 128.496\n",
"Epoch 26 of 100, Loss: 157.841\n",
"Epoch 27 of 100, Loss: 369.787\n",
"Epoch 28 of 100, Loss: 94.269\n",
"Epoch 29 of 100, Loss: 134.898\n",
"Epoch 30 of 100, Loss: 74.488\n",
"Epoch 31 of 100, Loss: 214.186\n",
"Epoch 32 of 100, Loss: 112.555\n",
"Epoch 33 of 100, Loss: 78.977\n",
"Epoch 34 of 100, Loss: 134.299\n",
"Epoch 35 of 100, Loss: 35.186\n",
"Epoch 36 of 100, Loss: 76.879\n",
"Epoch 37 of 100, Loss: 195.191\n",
"Epoch 38 of 100, Loss: 113.689\n",
"Epoch 39 of 100, Loss: 104.923\n",
"Epoch 40 of 100, Loss: 155.706\n",
"Epoch 41 of 100, Loss: 115.923\n",
"Epoch 42 of 100, Loss: 67.577\n",
"Epoch 43 of 100, Loss: 105.407\n",
"Epoch 44 of 100, Loss: 125.645\n",
"Epoch 45 of 100, Loss: 133.77\n",
"Epoch 46 of 100, Loss: 149.836\n",
"Epoch 47 of 100, Loss: 173.448\n",
"Epoch 48 of 100, Loss: 110.101\n",
"Epoch 49 of 100, Loss: 138.044\n",
"Epoch 50 of 100, Loss: 153.611\n",
"Epoch 51 of 100, Loss: 61.501\n",
"Epoch 52 of 100, Loss: 71.998\n",
"Epoch 53 of 100, Loss: 188.658\n",
"Epoch 54 of 100, Loss: 80.28\n",
"Epoch 55 of 100, Loss: 95.933\n",
"Epoch 56 of 100, Loss: 136.835\n",
"Epoch 57 of 100, Loss: 129.396\n",
"Epoch 58 of 100, Loss: 66.494\n",
"Epoch 59 of 100, Loss: 111.14\n",
"Epoch 60 of 100, Loss: 176.363\n",
"Epoch 61 of 100, Loss: 75.573\n",
"Epoch 62 of 100, Loss: 53.56\n",
"Epoch 63 of 100, Loss: 89.78\n",
"Epoch 64 of 100, Loss: 93.642\n",
"Epoch 65 of 100, Loss: 105.566\n",
"Epoch 66 of 100, Loss: 159.151\n",
"Epoch 67 of 100, Loss: 89.265\n",
"Epoch 68 of 100, Loss: 121.349\n",
"Epoch 69 of 100, Loss: 98.278\n",
"Epoch 70 of 100, Loss: 249.79\n",
"Epoch 71 of 100, Loss: 111.768\n",
"Epoch 72 of 100, Loss: 137.894\n",
"Epoch 73 of 100, Loss: 131.131\n",
"Epoch 74 of 100, Loss: 83.891\n",
"Epoch 75 of 100, Loss: 99.166\n",
"Epoch 76 of 100, Loss: 219.011\n",
"Epoch 77 of 100, Loss: 62.321\n",
"Epoch 78 of 100, Loss: 109.47\n",
"Epoch 79 of 100, Loss: 122.896\n",
"Epoch 80 of 100, Loss: 58.683\n",
"Epoch 81 of 100, Loss: 199.865\n",
"Epoch 82 of 100, Loss: 122.807\n",
"Epoch 83 of 100, Loss: 138.183\n",
"Epoch 84 of 100, Loss: 152.517\n",
"Epoch 85 of 100, Loss: 34.736\n",
"Epoch 86 of 100, Loss: 34.167\n",
"Epoch 87 of 100, Loss: 152.289\n",
"Epoch 88 of 100, Loss: 58.821\n",
"Epoch 89 of 100, Loss: 70.747\n",
"Epoch 90 of 100, Loss: 259.394\n",
"Epoch 91 of 100, Loss: 79.002\n",
"Epoch 92 of 100, Loss: 80.815\n",
"Epoch 93 of 100, Loss: 152.681\n",
"Epoch 94 of 100, Loss: 103.209\n",
"Epoch 95 of 100, Loss: 75.965\n",
"Epoch 96 of 100, Loss: 67.907\n",
"Epoch 97 of 100, Loss: 96.509\n",
"Epoch 98 of 100, Loss: 182.213\n",
"Epoch 99 of 100, Loss: 55.769\n",
"Epoch 100 of 100, Loss: 115.025\n",
"Epoch 1 of 10, Loss: 78.643\n",
"Epoch 2 of 10, Loss: 144.536\n",
"Epoch 3 of 10, Loss: 53.688\n",
"Epoch 4 of 10, Loss: 177.116\n",
"Epoch 5 of 10, Loss: 198.454\n",
"Epoch 6 of 10, Loss: 90.156\n",
"Epoch 7 of 10, Loss: 44.655\n",
"Epoch 8 of 10, Loss: 205.163\n",
"Epoch 9 of 10, Loss: 246.263\n",
"Epoch 10 of 10, Loss: 102.745\n",
"\n",
" RMSE: 11.100189228700662\n"
" RMSE: 11.113094884527147\n"
]
}
],
"source": [
"train_dataset = wd_dataset['train_dataset']\n",
"model.fit(dataset=train_dataset, n_epochs=100, batch_size=64)\n",
"model.fit(dataset=train_dataset, n_epochs=10, batch_size=64)\n",
"\n",
"test_dataset = wd_dataset['test_dataset']\n",
"pred = model.predict(test_dataset)\n",
......
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